extract.bestM

This functions extracts the best fitting model(s) according to the Bayesian information criterion (BIC).

Implements three families of parsimonious hidden Markov models (HMMs) for matrix-variate longitudinal data using the Expectation-Conditional Maximization (ECM) algorithm. The package supports matrix-variate normal, t, and contaminated normal distributions as emission distributions. For each hidden state, parsimony is achieved through the eigen-decomposition of the covariance matrices associated with the emission distribution. This approach results in a comprehensive set of 98 parsimonious HMMs for each type of emission distribution. Atypical matrix detection is also supported, utilizing the fitted (heavy-tailed) models.

Salvatore D. Tomarchio

MatrixHMM

Parsimonious Families of Hidden Markov Models for Matrix-Variate
Longitudinal Data

extract.bestM function

<dl><dt>results</dt>
<dd>The output of the <code>Eigen.HMM_fit()</code> function.</dd>
<dt>top</dt>
<dd>Integer. Specifies the number of top-ranked models to display based on the Bayesian Information Criterion (BIC).</dd></dl>

Arguments

Selection of the best fitting model(s) — extract.bestM

<dl>

<dt>results</dt>
<dd>The output of the <code>Eigen.HMM_fit()</code> function.</dd>


<dt>top</dt>
<dd>Integer. Specifies the number of top-ranked models to display based on the Bayesian Information Criterion (BIC).</dd>

</dl>

extract.bestM: Selection of the best fitting model(s)

Description

Usage

Value

Arguments

Examples